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NetCDF Packed int16 with scale_factor / add_offset (.nc)

The same temperature field stored as int16 and recovered through the CF packing attributes scale_factor and add_offset. This is the failure that parses cleanly and is silently wrong: a reader that ignores the attributes returns values around -2000 instead of 260-292 K.

Preview — schema + first 7 rowsnc
FieldValue
Stored dtypeint16
scale_factor0.01
add_offset280.0
Unpack rulevalue = stored * scale_factor + add_offset
Stored[0,0,0]-1000
Unpacked[0,0,0]270.0000 K
_FillValue-32768 (applies to the STORED value, before unpacking)
A reader that ignores scale_factor/add_offset returns integers near -2000, not kelvin.

Specifications

Format
NetCDF-3 classic
Stored Type
int16
Scale Factor
0.01
Add Offset
280
Unpacked Type
float64
Packed Range
-1750 to 1250
Quantisation Step
0.01 K

Testing contract

Expected to pass
Scenario
Read the tas variable and apply the CF packing convention, then compare against the paired unpacked CSV.
Expected result
Unpacked values land within 0.005 K of the CSV's original_kelvin column; a reader that skips packing returns raw integers between -2000 and 1200 instead.

What is a .nc file?

NetCDF (.nc, Network Common Data Form) is a binary, self-describing format for array-oriented scientific data. It stores multidimensional variables (like temperature over latitude, longitude, and time) with named dimensions, units, and metadata attributes, and is a standard in climate, ocean, and geoscience.

How to use this file

Use an example .nc file to test NetCDF readers (netCDF4, xarray, Panoply), CF-convention validators, and gridded-data pipelines, or to verify dimension and variable extraction.

How to use this file for testing

“NetCDF Packed int16 with scale_factor / add_offset (.nc)” is a deterministic Novus Examples fixture for Scientific data, Serialization testing. Citation catalogs (BibTeX, RIS), chemistry structures (MDL Molfile, PDB), and gridded binary data (NetCDF, FITS) — for testing reference managers, molecule viewers, and scientific-data loaders.

Documented properties for this file: NetCDF-3 classic. Compare results against paired or grouped companions on this page when present (clean↔damaged, searchable↔scanned, or format twins) so scores stay reproducible across runs.

Download the file once, keep the path stable in CI or local scripts, and treat the spec table as the contract: dimensions, seeds, field lists, and roles are intentional. Corrupt or invalid samples are labelled as such — expect parsers to fail loudly rather than silently accept them.

Scientific fixtures are small, valid, and fully synthetic — no real organism, patient, sample, or observation. Point your parser or loader at the file and check it reads the documented records, variables, or headers; binary formats ship a readable twin or metadata listing for comparison.

Generated by generation/scientific.py. Free for any use, no attribution required — license.